Overview

Brought to you by YData

Dataset statistics

Number of variables33
Number of observations1904354
Missing cells30
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory165.6 MiB
Average record size in memory91.2 B

Variable types

Numeric19
Boolean1
Categorical10
DateTime3

Alerts

NOMBRE has a high cardinality: 955 distinct values High cardinality
NOMLARGO has a high cardinality: 983 distinct values High cardinality
RFC has a high cardinality: 762 distinct values High cardinality
NLINEA has a high cardinality: 1015 distinct values High cardinality
FLIQ has a high cardinality: 5913 distinct values High cardinality
IINSTR has a high cardinality: 132 distinct values High cardinality
AÑO_FLIQ is highly overall correlated with AÑO_FVENCE and 1 other fieldsHigh correlation
AÑO_FVENCE is highly overall correlated with AÑO_FLIQ and 1 other fieldsHigh correlation
AÑO_OPE is highly overall correlated with AÑO_FLIQ and 1 other fieldsHigh correlation
CPZO_A is highly overall correlated with CPZO_DEHigh correlation
CPZO_DE is highly overall correlated with CPZO_AHigh correlation
DIA_FLIQ is highly overall correlated with DIA_FVENCE and 1 other fieldsHigh correlation
DIA_FVENCE is highly overall correlated with DIA_FLIQ and 1 other fieldsHigh correlation
DIA_OPE is highly overall correlated with DIA_FLIQ and 1 other fieldsHigh correlation
ESPROVEED is highly overall correlated with TIPO_PERSONAHigh correlation
ICONTRATO is highly overall correlated with NUMERO_CLIENTEHigh correlation
ITINSTR is highly overall correlated with MONEDA and 1 other fieldsHigh correlation
MES_FLIQ is highly overall correlated with MES_FVENCE and 1 other fieldsHigh correlation
MES_FVENCE is highly overall correlated with MES_FLIQ and 1 other fieldsHigh correlation
MES_OPE is highly overall correlated with MES_FLIQ and 1 other fieldsHigh correlation
MONEDA is highly overall correlated with ITINSTR and 1 other fieldsHigh correlation
MONTO is highly overall correlated with MONTO_ASIGNADO and 1 other fieldsHigh correlation
MONTO_ASIGNADO is highly overall correlated with MONTO and 1 other fieldsHigh correlation
MONTO_REAL is highly overall correlated with MONTO and 1 other fieldsHigh correlation
NTINSTR is highly overall correlated with ITINSTR and 1 other fieldsHigh correlation
NUMERO_CLIENTE is highly overall correlated with ICONTRATOHigh correlation
TIPO_PERSONA is highly overall correlated with ESPROVEEDHigh correlation
IINSTR is highly imbalanced (69.1%) Imbalance
ITINSTR is highly imbalanced (72.4%) Imbalance
NTINSTR is highly imbalanced (71.1%) Imbalance
MONEDA is highly imbalanced (> 99.9%) Imbalance
CPZO_DE is highly skewed (γ1 = 236.5402493) Skewed
CPZO_A is highly skewed (γ1 = 235.002316) Skewed
PLAZOREF is highly skewed (γ1 = 51.32852076) Skewed
AÑO_FVENCE is highly skewed (γ1 = 133.3760011) Skewed
TASA is highly skewed (γ1 = 104.6177444) Skewed
MONTO is highly skewed (γ1 = 133.805481) Skewed
MONTO_REAL is highly skewed (γ1 = 94.71003723) Skewed
PLAZOREF has 1837810 (96.5%) zeros Zeros
TASA has 55531 (2.9%) zeros Zeros
MONTO_ASIGNADO has 28357 (1.5%) zeros Zeros

Reproduction

Analysis started2025-02-26 23:21:57.753927
Analysis finished2025-02-26 23:25:12.857304
Duration3 minutes and 15.1 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

NUMERO_CLIENTE
Real number (ℝ)

High correlation 

Distinct1016
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean816827.81
Minimum5
Maximum9001793
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 MiB
2025-02-26T17:25:12.991522image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile9080
Q1909016
median1060445
Q31063955
95-th percentile1064796
Maximum9001793
Range9001788
Interquartile range (IQR)154939

Descriptive statistics

Standard deviation425303.94
Coefficient of variation (CV)0.52067759
Kurtosis-0.010963404
Mean816827.81
Median Absolute Deviation (MAD)4435
Skewness-1.2781954
Sum1.5555293 × 1012
Variance1.8088344 × 1011
MonotonicityNot monotonic
2025-02-26T17:25:13.179180image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000 119698
 
6.3%
30000 118531
 
6.2%
1064343 106285
 
5.6%
989141 47298
 
2.5%
1064456 42101
 
2.2%
91000 23003
 
1.2%
1062065 22769
 
1.2%
71000 20903
 
1.1%
920095 19501
 
1.0%
905487 17529
 
0.9%
Other values (1006) 1366736
71.8%
ValueCountFrequency (%)
5 24
 
< 0.1%
31 10148
0.5%
80 5689
0.3%
102 3550
 
0.2%
145 7826
0.4%
161 6797
0.4%
315 9346
0.5%
412 13799
0.7%
811 4171
 
0.2%
821 4342
 
0.2%
ValueCountFrequency (%)
9001793 2
 
< 0.1%
6009449 11
 
< 0.1%
2002989 1
 
< 0.1%
1065877 32
 
< 0.1%
1065866 103
< 0.1%
1065862 88
< 0.1%
1065860 15
 
< 0.1%
1065859 101
< 0.1%
1065855 4
 
< 0.1%
1065852 175
< 0.1%

ESPROVEED
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
True
1185079 
False
719275 
ValueCountFrequency (%)
True 1185079
62.2%
False 719275
37.8%
2025-02-26T17:25:13.350362image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

TIPO_PERSONA
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Moral Nacional no Gravable
1382113 
Cuentas Propias
325775 
Moral Nacional Gravable
164723 
Moral Nal.No Gravable (Fondos)
 
30654
Moral Extranjera Pais c/tratad
 
1089

Length

Max length30
Median length26
Mean length23.925427
Min length15

Characters and Unicode

Total characters45562482
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMoral Nacional no Gravable
2nd rowCuentas Propias
3rd rowMoral Nacional no Gravable
4th rowCuentas Propias
5th rowMoral Nacional no Gravable

Common Values

ValueCountFrequency (%)
Moral Nacional no Gravable 1382113
72.6%
Cuentas Propias 325775
 
17.1%
Moral Nacional Gravable 164723
 
8.6%
Moral Nal.No Gravable (Fondos) 30654
 
1.6%
Moral Extranjera Pais c/tratad 1089
 
0.1%

Length

2025-02-26T17:25:13.559953image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-26T17:25:13.754408image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
moral 1578579
23.2%
gravable 1577490
23.2%
nacional 1546836
22.7%
no 1382113
20.3%
cuentas 325775
 
4.8%
propias 325775
 
4.8%
nal.no 30654
 
0.5%
fondos 30654
 
0.5%
extranjera 1089
 
< 0.1%
pais 1089
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a 8514880
18.7%
o 4925265
10.8%
4896789
10.7%
l 4733559
10.4%
r 3485111
 
7.6%
n 3286467
 
7.2%
e 1904354
 
4.2%
i 1873700
 
4.1%
N 1608144
 
3.5%
M 1578579
 
3.5%
Other values (19) 8755634
19.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45562482
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 8514880
18.7%
o 4925265
10.8%
4896789
10.7%
l 4733559
10.4%
r 3485111
 
7.6%
n 3286467
 
7.2%
e 1904354
 
4.2%
i 1873700
 
4.1%
N 1608144
 
3.5%
M 1578579
 
3.5%
Other values (19) 8755634
19.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45562482
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 8514880
18.7%
o 4925265
10.8%
4896789
10.7%
l 4733559
10.4%
r 3485111
 
7.6%
n 3286467
 
7.2%
e 1904354
 
4.2%
i 1873700
 
4.1%
N 1608144
 
3.5%
M 1578579
 
3.5%
Other values (19) 8755634
19.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45562482
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 8514880
18.7%
o 4925265
10.8%
4896789
10.7%
l 4733559
10.4%
r 3485111
 
7.6%
n 3286467
 
7.2%
e 1904354
 
4.2%
i 1873700
 
4.1%
N 1608144
 
3.5%
M 1578579
 
3.5%
Other values (19) 8755634
19.2%

NOMBRE
Categorical

High cardinality 

Distinct955
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.7 MiB
PASIVO
 
119698
Proveedor Papel NAFIN
 
118531
PROGRAMA CETESDIRECTO
 
106285
FIDEICOMISOS
 
70534
SANTANDER
 
47298
Other values (950)
1442008 

Length

Max length26
Median length21
Mean length13.788014
Min length2

Characters and Unicode

Total characters26257260
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)< 0.1%

Sample

1st rowBANXICO
2nd rowGARANTIAS CREDITO
3rd rowBANXICO
4th rowGARANTIAS CREDITO
5th rowBANXICO

Common Values

ValueCountFrequency (%)
PASIVO 119698
 
6.3%
Proveedor Papel NAFIN 118531
 
6.2%
PROGRAMA CETESDIRECTO 106285
 
5.6%
FIDEICOMISOS 70534
 
3.7%
SANTANDER 47298
 
2.5%
SEGUROS BBVA BANCOMER SA. 42101
 
2.2%
Guber Vencimiento 23003
 
1.2%
SEGUROS BBVA BANCOMER 22769
 
1.2%
TESORERIA 20903
 
1.1%
BBVA MEXICO 19501
 
1.0%
Other values (945) 1313731
69.0%

Length

2025-02-26T17:25:13.951096image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
167720
 
4.1%
nafin 130325
 
3.2%
pasivo 119698
 
2.9%
proveedor 118532
 
2.9%
papel 118531
 
2.9%
fideicomisos 117807
 
2.9%
programa 106286
 
2.6%
cetesdirecto 106285
 
2.6%
fiso 101163
 
2.5%
bbva 88801
 
2.2%
Other values (1121) 2907643
71.2%

Most occurring characters

ValueCountFrequency (%)
2240493
 
8.5%
A 2175471
 
8.3%
E 1792984
 
6.8%
I 1772924
 
6.8%
S 1626491
 
6.2%
O 1623945
 
6.2%
R 1293985
 
4.9%
N 1235662
 
4.7%
C 1169398
 
4.5%
B 900969
 
3.4%
Other values (57) 10424938
39.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26257260
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2240493
 
8.5%
A 2175471
 
8.3%
E 1792984
 
6.8%
I 1772924
 
6.8%
S 1626491
 
6.2%
O 1623945
 
6.2%
R 1293985
 
4.9%
N 1235662
 
4.7%
C 1169398
 
4.5%
B 900969
 
3.4%
Other values (57) 10424938
39.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26257260
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2240493
 
8.5%
A 2175471
 
8.3%
E 1792984
 
6.8%
I 1772924
 
6.8%
S 1626491
 
6.2%
O 1623945
 
6.2%
R 1293985
 
4.9%
N 1235662
 
4.7%
C 1169398
 
4.5%
B 900969
 
3.4%
Other values (57) 10424938
39.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26257260
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2240493
 
8.5%
A 2175471
 
8.3%
E 1792984
 
6.8%
I 1772924
 
6.8%
S 1626491
 
6.2%
O 1623945
 
6.2%
R 1293985
 
4.9%
N 1235662
 
4.7%
C 1169398
 
4.5%
B 900969
 
3.4%
Other values (57) 10424938
39.7%

NOMLARGO
Categorical

High cardinality 

Distinct983
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.7 MiB
PASIVO
 
119698
Proveedor Papel para Emision de Papel
 
118531
NAFIN, SNC IBD EN SU CALIDAD DE COM DE LOS CTES DEL CANAL DE DIST CETES
 
106285
SEGUROS BBVA BANCOMER SA DE CV GRUPO FINANCIERO BBVA BANCOMER
 
64870
BANCO SANTANDER DE NEGOCIOS MEXICO,S.A.
 
47298
Other values (978)
1447672 

Length

Max length71
Median length63
Mean length46.876481
Min length6

Characters and Unicode

Total characters89269415
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique93 ?
Unique (%)< 0.1%

Sample

1st rowBANCO DE MEXICO
2nd rowGARANTIAS CREDITO
3rd rowBANCO DE MEXICO
4th rowGARANTIAS CREDITO
5th rowBANCO DE MEXICO

Common Values

ValueCountFrequency (%)
PASIVO 119698
 
6.3%
Proveedor Papel para Emision de Papel 118531
 
6.2%
NAFIN, SNC IBD EN SU CALIDAD DE COM DE LOS CTES DEL CANAL DE DIST CETES 106285
 
5.6%
SEGUROS BBVA BANCOMER SA DE CV GRUPO FINANCIERO BBVA BANCOMER 64870
 
3.4%
BANCO SANTANDER DE NEGOCIOS MEXICO,S.A. 47298
 
2.5%
Gubernamental Vencimiento MN. 23003
 
1.2%
TESORERIA FONDEO 20903
 
1.1%
FDO. PENS. Y PRIMAS ANTIGUEDAD N. /50145 20803
 
1.1%
BBVA M�XICO, S.A. INSTITUCI�N DE BANCA MULTIPLE, GRUPO FINANCIERO BBVA 19501
 
1.0%
CBM BANCO, SA, IBM, INTEGRANTE DEL GRUPO FINANCIERO CITIBANAMEX 17529
 
0.9%
Other values (973) 1345933
70.7%

Length

2025-02-26T17:25:14.169068image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 2071300
 
13.9%
s.a 654762
 
4.4%
c.v 526507
 
3.5%
del 322047
 
2.2%
nafin 298951
 
2.0%
en 267337
 
1.8%
fondo 243974
 
1.6%
papel 237062
 
1.6%
bbva 209670
 
1.4%
grupo 200679
 
1.3%
Other values (1338) 9849321
66.2%

Most occurring characters

ValueCountFrequency (%)
13072346
14.6%
A 7419956
 
8.3%
E 6782250
 
7.6%
I 5957830
 
6.7%
O 5258738
 
5.9%
N 5030501
 
5.6%
C 4855044
 
5.4%
S 4808265
 
5.4%
D 4647667
 
5.2%
. 3918633
 
4.4%
Other values (59) 27518185
30.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 89269415
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
13072346
14.6%
A 7419956
 
8.3%
E 6782250
 
7.6%
I 5957830
 
6.7%
O 5258738
 
5.9%
N 5030501
 
5.6%
C 4855044
 
5.4%
S 4808265
 
5.4%
D 4647667
 
5.2%
. 3918633
 
4.4%
Other values (59) 27518185
30.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 89269415
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
13072346
14.6%
A 7419956
 
8.3%
E 6782250
 
7.6%
I 5957830
 
6.7%
O 5258738
 
5.9%
N 5030501
 
5.6%
C 4855044
 
5.4%
S 4808265
 
5.4%
D 4647667
 
5.2%
. 3918633
 
4.4%
Other values (59) 27518185
30.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 89269415
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
13072346
14.6%
A 7419956
 
8.3%
E 6782250
 
7.6%
I 5957830
 
6.7%
O 5258738
 
5.9%
N 5030501
 
5.6%
C 4855044
 
5.4%
S 4808265
 
5.4%
D 4647667
 
5.2%
. 3918633
 
4.4%
Other values (59) 27518185
30.8%

RFC
Categorical

High cardinality 

Distinct762
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.7 MiB
NFI3406305T0
574866 
SBB961118TIA
 
64870
BSM941122TE6
 
47298
CJF950204TL0
 
46913
INF7205011ZA
 
38056
Other values (757)
1132351 

Length

Max length13
Median length12
Mean length12.000781
Min length12

Characters and Unicode

Total characters22853735
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)< 0.1%

Sample

1st rowBNM840515VB2
2nd rowNFI3406305T0
3rd rowBNM840515VB2
4th rowNFI3406305T0
5th rowBNM840515VB2

Common Values

ValueCountFrequency (%)
NFI3406305T0 574866
30.2%
SBB961118TIA 64870
 
3.4%
BSM941122TE6 47298
 
2.5%
CJF950204TL0 46913
 
2.5%
INF7205011ZA 38056
 
2.0%
NFF930518F76 23781
 
1.2%
DBM000228J35 23073
 
1.2%
BBA830831LJ2 22886
 
1.2%
IPA9901206D2 21976
 
1.2%
IMS421231I45 21569
 
1.1%
Other values (752) 1019066
53.5%

Length

2025-02-26T17:25:14.368579image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nfi3406305t0 574866
30.2%
sbb961118tia 64870
 
3.4%
bsm941122te6 47298
 
2.5%
cjf950204tl0 46913
 
2.5%
inf7205011za 38056
 
2.0%
nff930518f76 23781
 
1.2%
dbm000228j35 23073
 
1.2%
bba830831lj2 22886
 
1.2%
ipa9901206d2 21976
 
1.2%
ims421231i45 21569
 
1.1%
Other values (752) 1019066
53.5%

Most occurring characters

ValueCountFrequency (%)
0 3998708
17.5%
3 1700014
 
7.4%
1 1609858
 
7.0%
5 1337309
 
5.9%
6 1231105
 
5.4%
F 1157422
 
5.1%
4 1131869
 
5.0%
2 1110133
 
4.9%
I 998597
 
4.4%
9 980892
 
4.3%
Other values (27) 7597828
33.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22853735
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3998708
17.5%
3 1700014
 
7.4%
1 1609858
 
7.0%
5 1337309
 
5.9%
6 1231105
 
5.4%
F 1157422
 
5.1%
4 1131869
 
5.0%
2 1110133
 
4.9%
I 998597
 
4.4%
9 980892
 
4.3%
Other values (27) 7597828
33.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22853735
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3998708
17.5%
3 1700014
 
7.4%
1 1609858
 
7.0%
5 1337309
 
5.9%
6 1231105
 
5.4%
F 1157422
 
5.1%
4 1131869
 
5.0%
2 1110133
 
4.9%
I 998597
 
4.4%
9 980892
 
4.3%
Other values (27) 7597828
33.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22853735
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3998708
17.5%
3 1700014
 
7.4%
1 1609858
 
7.0%
5 1337309
 
5.9%
6 1231105
 
5.4%
F 1157422
 
5.1%
4 1131869
 
5.0%
2 1110133
 
4.9%
I 998597
 
4.4%
9 980892
 
4.3%
Other values (27) 7597828
33.2%

NLINEA
Categorical

High cardinality 

Distinct1015
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.7 MiB
00020000
 
119698
00030000
 
118531
1064343
 
106285
989141
 
47298
1064456
 
42101
Other values (1010)
1470441 

Length

Max length8
Median length7
Mean length7.1961836
Min length1

Characters and Unicode

Total characters13704081
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique112 ?
Unique (%)< 0.1%

Sample

1st row01040153
2nd row00051000
3rd row01040153
4th row00051000
5th row01040153

Common Values

ValueCountFrequency (%)
00020000 119698
 
6.3%
00030000 118531
 
6.2%
1064343 106285
 
5.6%
989141 47298
 
2.5%
1064456 42101
 
2.2%
00009070 30654
 
1.6%
91000 23003
 
1.2%
01062065 22769
 
1.2%
00071000 20903
 
1.1%
920095 19501
 
1.0%
Other values (1005) 1353611
71.1%

Length

2025-02-26T17:25:14.545743image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00020000 119698
 
6.3%
00030000 118531
 
6.2%
1064343 106285
 
5.6%
989141 47298
 
2.5%
1064456 42101
 
2.2%
00009070 30654
 
1.6%
91000 23003
 
1.2%
01062065 22769
 
1.2%
00071000 20903
 
1.1%
920095 19501
 
1.0%
Other values (1004) 1353581
71.1%

Most occurring characters

ValueCountFrequency (%)
0 4881940
35.6%
1 2056975
15.0%
6 1442421
 
10.5%
4 1157847
 
8.4%
3 1024815
 
7.5%
5 687489
 
5.0%
9 670445
 
4.9%
2 656380
 
4.8%
7 614103
 
4.5%
8 511636
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13704081
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4881940
35.6%
1 2056975
15.0%
6 1442421
 
10.5%
4 1157847
 
8.4%
3 1024815
 
7.5%
5 687489
 
5.0%
9 670445
 
4.9%
2 656380
 
4.8%
7 614103
 
4.5%
8 511636
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13704081
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4881940
35.6%
1 2056975
15.0%
6 1442421
 
10.5%
4 1157847
 
8.4%
3 1024815
 
7.5%
5 687489
 
5.0%
9 670445
 
4.9%
2 656380
 
4.8%
7 614103
 
4.5%
8 511636
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13704081
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4881940
35.6%
1 2056975
15.0%
6 1442421
 
10.5%
4 1157847
 
8.4%
3 1024815
 
7.5%
5 687489
 
5.0%
9 670445
 
4.9%
2 656380
 
4.8%
7 614103
 
4.5%
8 511636
 
3.7%

ICONTRATO
Real number (ℝ)

High correlation 

Distinct1016
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean816827.81
Minimum5
Maximum9001793
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 MiB
2025-02-26T17:25:14.744231image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile9080
Q1909016
median1060445
Q31063955
95-th percentile1064796
Maximum9001793
Range9001788
Interquartile range (IQR)154939

Descriptive statistics

Standard deviation425303.94
Coefficient of variation (CV)0.52067759
Kurtosis-0.010963404
Mean816827.81
Median Absolute Deviation (MAD)4435
Skewness-1.2781954
Sum1.5555293 × 1012
Variance1.8088344 × 1011
MonotonicityNot monotonic
2025-02-26T17:25:14.954296image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000 119698
 
6.3%
30000 118531
 
6.2%
1064343 106285
 
5.6%
989141 47298
 
2.5%
1064456 42101
 
2.2%
91000 23003
 
1.2%
1062065 22769
 
1.2%
71000 20903
 
1.1%
920095 19501
 
1.0%
905487 17529
 
0.9%
Other values (1006) 1366736
71.8%
ValueCountFrequency (%)
5 24
 
< 0.1%
31 10148
0.5%
80 5689
0.3%
102 3550
 
0.2%
145 7826
0.4%
161 6797
0.4%
315 9346
0.5%
412 13799
0.7%
811 4171
 
0.2%
821 4342
 
0.2%
ValueCountFrequency (%)
9001793 2
 
< 0.1%
6009449 11
 
< 0.1%
2002989 1
 
< 0.1%
1065877 32
 
< 0.1%
1065866 103
< 0.1%
1065862 88
< 0.1%
1065860 15
 
< 0.1%
1065859 101
< 0.1%
1065855 4
 
< 0.1%
1065852 175
< 0.1%

IORDEN
Real number (ℝ)

Distinct927621
Distinct (%)48.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean528961.8
Minimum1
Maximum1438187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 MiB
2025-02-26T17:25:15.165418image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile45909.65
Q1279794
median550309
Q3780838.75
95-th percentile956904
Maximum1438187
Range1438186
Interquartile range (IQR)501044.75

Descriptive statistics

Standard deviation290419.62
Coefficient of variation (CV)0.54903705
Kurtosis-1.188255
Mean528961.8
Median Absolute Deviation (MAD)248728
Skewness-0.14691818
Sum1.0073305 × 1012
Variance8.4343558 × 1010
MonotonicityNot monotonic
2025-02-26T17:25:15.580987image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 244
 
< 0.1%
3 238
 
< 0.1%
4 224
 
< 0.1%
9 211
 
< 0.1%
5 210
 
< 0.1%
7 207
 
< 0.1%
8 206
 
< 0.1%
10 196
 
< 0.1%
6 188
 
< 0.1%
13 180
 
< 0.1%
Other values (927611) 1902250
99.9%
ValueCountFrequency (%)
1 171
< 0.1%
2 244
< 0.1%
3 238
< 0.1%
4 224
< 0.1%
5 210
< 0.1%
6 188
< 0.1%
7 207
< 0.1%
8 206
< 0.1%
9 211
< 0.1%
10 196
< 0.1%
ValueCountFrequency (%)
1438187 1
< 0.1%
1437908 1
< 0.1%
1437814 1
< 0.1%
1437719 1
< 0.1%
1437717 1
< 0.1%
1437701 1
< 0.1%
1437685 1
< 0.1%
1437683 1
< 0.1%
1437655 1
< 0.1%
1437638 1
< 0.1%

FOPER
Date

Distinct5909
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size14.5 MiB
Minimum1993-09-09 00:00:00
Maximum2024-11-29 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-26T17:25:15.808088image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:25:15.996162image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

AÑO_OPE
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.0524
Minimum1993
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 MiB
2025-02-26T17:25:16.156124image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1993
5-th percentile2004
Q12011
median2016
Q32020
95-th percentile2024
Maximum2024
Range31
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.9695826
Coefficient of variation (CV)0.002962495
Kurtosis-0.77563945
Mean2015.0524
Median Absolute Deviation (MAD)5
Skewness-0.36351655
Sum3.837373 × 109
Variance35.635916
MonotonicityNot monotonic
2025-02-26T17:25:16.319310image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2016 118371
 
6.2%
2024 113970
 
6.0%
2018 111914
 
5.9%
2017 110129
 
5.8%
2020 109495
 
5.7%
2019 107578
 
5.6%
2023 106992
 
5.6%
2015 100067
 
5.3%
2021 98737
 
5.2%
2022 98074
 
5.1%
Other values (19) 829027
43.5%
ValueCountFrequency (%)
1993 10
 
< 0.1%
1994 29
 
< 0.1%
1998 5
 
< 0.1%
1999 20
 
< 0.1%
2000 132
 
< 0.1%
2001 170
 
< 0.1%
2002 39059
2.1%
2003 40678
2.1%
2004 37969
2.0%
2005 40782
2.1%
ValueCountFrequency (%)
2024 113970
6.0%
2023 106992
5.6%
2022 98074
5.1%
2021 98737
5.2%
2020 109495
5.7%
2019 107578
5.6%
2018 111914
5.9%
2017 110129
5.8%
2016 118371
6.2%
2015 100067
5.3%

MES_OPE
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5188542
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-02-26T17:25:16.468579image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4059758
Coefficient of variation (CV)0.52248076
Kurtosis-1.1815875
Mean6.5188542
Median Absolute Deviation (MAD)3
Skewness-0.026526235
Sum12414206
Variance11.600671
MonotonicityNot monotonic
2025-02-26T17:25:16.638385image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 170861
9.0%
8 169714
8.9%
7 166217
8.7%
5 163971
8.6%
6 162337
8.5%
1 160274
8.4%
9 157567
8.3%
3 157049
8.2%
4 152720
8.0%
11 152394
8.0%
Other values (2) 291250
15.3%
ValueCountFrequency (%)
1 160274
8.4%
2 144125
7.6%
3 157049
8.2%
4 152720
8.0%
5 163971
8.6%
6 162337
8.5%
7 166217
8.7%
8 169714
8.9%
9 157567
8.3%
10 170861
9.0%
ValueCountFrequency (%)
12 147125
7.7%
11 152394
8.0%
10 170861
9.0%
9 157567
8.3%
8 169714
8.9%
7 166217
8.7%
6 162337
8.5%
5 163971
8.6%
4 152720
8.0%
3 157049
8.2%

DIA_OPE
Real number (ℝ)

High correlation 

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.873441
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-02-26T17:25:16.818512image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.783561
Coefficient of variation (CV)0.55334952
Kurtosis-1.2002696
Mean15.873441
Median Absolute Deviation (MAD)8
Skewness0.007462111
Sum30228651
Variance77.150943
MonotonicityNot monotonic
2025-02-26T17:25:17.030119image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
8 65263
 
3.4%
28 65202
 
3.4%
13 65034
 
3.4%
11 64957
 
3.4%
14 64674
 
3.4%
22 64627
 
3.4%
27 64270
 
3.4%
26 64228
 
3.4%
23 63984
 
3.4%
9 63817
 
3.4%
Other values (21) 1258298
66.1%
ValueCountFrequency (%)
1 53275
2.8%
2 57544
3.0%
3 63319
3.3%
4 63328
3.3%
5 62194
3.3%
6 62329
3.3%
7 63775
3.3%
8 65263
3.4%
9 63817
3.4%
10 63425
3.3%
ValueCountFrequency (%)
31 38818
2.0%
30 60148
3.2%
29 59871
3.1%
28 65202
3.4%
27 64270
3.4%
26 64228
3.4%
25 59091
3.1%
24 62746
3.3%
23 63984
3.4%
22 64627
3.4%

FLIQ
Categorical

High cardinality 

Distinct5913
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2002-02-28 00:00:00
 
1412
2024-06-13 00:00:00
 
659
2024-11-28 00:00:00
 
636
2016-09-08 00:00:00
 
611
2024-06-20 00:00:00
 
610
Other values (5908)
1900426 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters36182726
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique69 ?
Unique (%)< 0.1%

Sample

1st row2020-11-12 00:00:00
2nd row2020-11-12 00:00:00
3rd row2020-11-12 00:00:00
4th row2020-11-12 00:00:00
5th row2020-04-01 00:00:00

Common Values

ValueCountFrequency (%)
2002-02-28 00:00:00 1412
 
0.1%
2024-06-13 00:00:00 659
 
< 0.1%
2024-11-28 00:00:00 636
 
< 0.1%
2016-09-08 00:00:00 611
 
< 0.1%
2024-06-20 00:00:00 610
 
< 0.1%
2018-12-13 00:00:00 605
 
< 0.1%
2024-01-11 00:00:00 597
 
< 0.1%
2017-01-26 00:00:00 597
 
< 0.1%
2016-07-21 00:00:00 593
 
< 0.1%
2024-11-22 00:00:00 587
 
< 0.1%
Other values (5903) 1897447
99.6%

Length

2025-02-26T17:25:17.215313image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00 1904354
50.0%
2002-02-28 1412
 
< 0.1%
2024-06-13 659
 
< 0.1%
2024-11-28 636
 
< 0.1%
2016-09-08 611
 
< 0.1%
2024-06-20 610
 
< 0.1%
2018-12-13 605
 
< 0.1%
2024-01-11 597
 
< 0.1%
2017-01-26 597
 
< 0.1%
2016-07-21 593
 
< 0.1%
Other values (5904) 1898034
49.8%

Most occurring characters

ValueCountFrequency (%)
0 16233477
44.9%
- 3808708
 
10.5%
: 3808708
 
10.5%
2 3767885
 
10.4%
1 2831166
 
7.8%
1904354
 
5.3%
3 688744
 
1.9%
4 585123
 
1.6%
9 527009
 
1.5%
8 523882
 
1.4%
Other values (3) 1503670
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36182726
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 16233477
44.9%
- 3808708
 
10.5%
: 3808708
 
10.5%
2 3767885
 
10.4%
1 2831166
 
7.8%
1904354
 
5.3%
3 688744
 
1.9%
4 585123
 
1.6%
9 527009
 
1.5%
8 523882
 
1.4%
Other values (3) 1503670
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36182726
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 16233477
44.9%
- 3808708
 
10.5%
: 3808708
 
10.5%
2 3767885
 
10.4%
1 2831166
 
7.8%
1904354
 
5.3%
3 688744
 
1.9%
4 585123
 
1.6%
9 527009
 
1.5%
8 523882
 
1.4%
Other values (3) 1503670
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36182726
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 16233477
44.9%
- 3808708
 
10.5%
: 3808708
 
10.5%
2 3767885
 
10.4%
1 2831166
 
7.8%
1904354
 
5.3%
3 688744
 
1.9%
4 585123
 
1.6%
9 527009
 
1.5%
8 523882
 
1.4%
Other values (3) 1503670
 
4.2%

AÑO_FLIQ
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.0525
Minimum1993
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 MiB
2025-02-26T17:25:17.379734image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1993
5-th percentile2004
Q12011
median2016
Q32020
95-th percentile2024
Maximum2024
Range31
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.9695696
Coefficient of variation (CV)0.0029624884
Kurtosis-0.77569569
Mean2015.0525
Median Absolute Deviation (MAD)5
Skewness-0.36350039
Sum3.8373733 × 109
Variance35.635761
MonotonicityNot monotonic
2025-02-26T17:25:17.556368image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2016 118364
 
6.2%
2024 113986
 
6.0%
2018 111928
 
5.9%
2017 110154
 
5.8%
2020 109487
 
5.7%
2019 107567
 
5.6%
2023 106998
 
5.6%
2015 100034
 
5.3%
2021 98754
 
5.2%
2022 98057
 
5.1%
Other values (19) 829025
43.5%
ValueCountFrequency (%)
1993 10
 
< 0.1%
1994 29
 
< 0.1%
1998 5
 
< 0.1%
1999 20
 
< 0.1%
2000 132
 
< 0.1%
2001 170
 
< 0.1%
2002 39033
2.0%
2003 40686
2.1%
2004 37986
2.0%
2005 40783
2.1%
ValueCountFrequency (%)
2024 113986
6.0%
2023 106998
5.6%
2022 98057
5.1%
2021 98754
5.2%
2020 109487
5.7%
2019 107567
5.6%
2018 111928
5.9%
2017 110154
5.8%
2016 118364
6.2%
2015 100034
5.3%

MES_FLIQ
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5232084
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-02-26T17:25:17.732026image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4059616
Coefficient of variation (CV)0.52212982
Kurtosis-1.1815418
Mean6.5232084
Median Absolute Deviation (MAD)3
Skewness-0.026989512
Sum12422498
Variance11.600574
MonotonicityNot monotonic
2025-02-26T17:25:17.886063image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 170593
9.0%
8 169585
8.9%
7 166485
8.7%
5 163869
8.6%
6 162310
8.5%
1 159437
8.4%
9 157550
8.3%
3 156928
8.2%
4 152766
8.0%
11 152739
8.0%
Other values (2) 292092
15.3%
ValueCountFrequency (%)
1 159437
8.4%
2 144318
7.6%
3 156928
8.2%
4 152766
8.0%
5 163869
8.6%
6 162310
8.5%
7 166485
8.7%
8 169585
8.9%
9 157550
8.3%
10 170593
9.0%
ValueCountFrequency (%)
12 147774
7.8%
11 152739
8.0%
10 170593
9.0%
9 157550
8.3%
8 169585
8.9%
7 166485
8.7%
6 162310
8.5%
5 163869
8.6%
4 152766
8.0%
3 156928
8.2%

DIA_FLIQ
Real number (ℝ)

High correlation 

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.892825
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-02-26T17:25:18.055709image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7822635
Coefficient of variation (CV)0.552593
Kurtosis-1.1989264
Mean15.892825
Median Absolute Deviation (MAD)8
Skewness0.0052201311
Sum30265564
Variance77.128153
MonotonicityNot monotonic
2025-02-26T17:25:18.201607image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
8 65398
 
3.4%
26 65042
 
3.4%
11 65026
 
3.4%
13 64838
 
3.4%
28 64834
 
3.4%
14 64756
 
3.4%
22 64679
 
3.4%
27 64399
 
3.4%
15 64186
 
3.4%
9 64081
 
3.4%
Other values (21) 1257115
66.0%
ValueCountFrequency (%)
1 53222
2.8%
2 57634
3.0%
3 62587
3.3%
4 63325
3.3%
5 61832
3.2%
6 61444
3.2%
7 63414
3.3%
8 65398
3.4%
9 64081
3.4%
10 63650
3.3%
ValueCountFrequency (%)
31 39155
2.1%
30 60377
3.2%
29 60210
3.2%
28 64834
3.4%
27 64399
3.4%
26 65042
3.4%
25 59005
3.1%
24 62484
3.3%
23 63996
3.4%
22 64679
3.4%

CPZO_DE
Real number (ℝ)

High correlation  Skewed 

Distinct8786
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean419.01227
Minimum0
Maximum999999
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.3 MiB
2025-02-26T17:25:18.396190image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q33
95-th percentile3016
Maximum999999
Range999999
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3360.996
Coefficient of variation (CV)8.0212353
Kurtosis69950.06
Mean419.01227
Median Absolute Deviation (MAD)0
Skewness236.54025
Sum7.9794769 × 108
Variance11296294
MonotonicityNot monotonic
2025-02-26T17:25:18.628958image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1148376
60.3%
3 275439
 
14.5%
4 36264
 
1.9%
28 28280
 
1.5%
7 21067
 
1.1%
2 18169
 
1.0%
5 9123
 
0.5%
14 5475
 
0.3%
91 4587
 
0.2%
6 4455
 
0.2%
Other values (8776) 353119
 
18.5%
ValueCountFrequency (%)
0 12
 
< 0.1%
1 1148376
60.3%
2 18169
 
1.0%
3 275439
 
14.5%
4 36264
 
1.9%
5 9123
 
0.5%
6 4455
 
0.2%
7 21067
 
1.1%
8 2687
 
0.1%
9 1467
 
0.1%
ValueCountFrequency (%)
999999 2
< 0.1%
999996 2
< 0.1%
999988 2
< 0.1%
999986 1
 
< 0.1%
999925 1
 
< 0.1%
999917 1
 
< 0.1%
999910 1
 
< 0.1%
999883 1
 
< 0.1%
999772 1
 
< 0.1%
999757 4
< 0.1%

CPZO_A
Real number (ℝ)

High correlation  Skewed 

Distinct8796
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean420.45603
Minimum0
Maximum999999
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.3 MiB
2025-02-26T17:25:18.820855image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q33
95-th percentile3020
Maximum999999
Range999999
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3368.9011
Coefficient of variation (CV)8.0124932
Kurtosis69299.013
Mean420.45603
Median Absolute Deviation (MAD)0
Skewness235.00232
Sum8.0069712 × 108
Variance11349495
MonotonicityNot monotonic
2025-02-26T17:25:18.989403image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1148123
60.3%
3 275438
 
14.5%
4 36264
 
1.9%
28 28282
 
1.5%
7 21069
 
1.1%
2 18170
 
1.0%
5 9123
 
0.5%
14 5474
 
0.3%
91 4774
 
0.3%
6 4455
 
0.2%
Other values (8786) 353182
 
18.5%
ValueCountFrequency (%)
0 12
 
< 0.1%
1 1148123
60.3%
2 18170
 
1.0%
3 275438
 
14.5%
4 36264
 
1.9%
5 9123
 
0.5%
6 4455
 
0.2%
7 21069
 
1.1%
8 2687
 
0.1%
9 1467
 
0.1%
ValueCountFrequency (%)
999999 2
< 0.1%
999996 2
< 0.1%
999988 2
< 0.1%
999986 1
 
< 0.1%
999925 1
 
< 0.1%
999917 1
 
< 0.1%
999910 1
 
< 0.1%
999883 1
 
< 0.1%
999772 1
 
< 0.1%
999757 4
< 0.1%

PLAZOREF
Real number (ℝ)

Skewed  Zeros 

Distinct47
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4694469
Minimum0
Maximum3612
Zeros1837810
Zeros (%)96.5%
Negative0
Negative (%)0.0%
Memory size3.6 MiB
2025-02-26T17:25:19.203251image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum3612
Range3612
Interquartile range (IQR)0

Descriptive statistics

Standard deviation16.482166
Coefficient of variation (CV)11.216579
Kurtosis8881.6522
Mean1.4694469
Median Absolute Deviation (MAD)0
Skewness51.328521
Sum2798347
Variance271.66181
MonotonicityNot monotonic
2025-02-26T17:25:19.405713image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 1837810
96.5%
1 40721
 
2.1%
182 9571
 
0.5%
28 8112
 
0.4%
91 7892
 
0.4%
30 170
 
< 0.1%
31 16
 
< 0.1%
27 4
 
< 0.1%
183 3
 
< 0.1%
1456 3
 
< 0.1%
Other values (37) 52
 
< 0.1%
ValueCountFrequency (%)
0 1837810
96.5%
1 40721
 
2.1%
25 2
 
< 0.1%
27 4
 
< 0.1%
28 8112
 
0.4%
29 3
 
< 0.1%
30 170
 
< 0.1%
31 16
 
< 0.1%
33 1
 
< 0.1%
91 7892
 
0.4%
ValueCountFrequency (%)
3612 2
< 0.1%
3605 2
< 0.1%
3598 2
< 0.1%
3591 1
 
< 0.1%
1456 3
< 0.1%
1445 2
< 0.1%
1410 2
< 0.1%
1409 2
< 0.1%
1291 2
< 0.1%
1096 2
< 0.1%

FVENCE
Date

Distinct6154
Distinct (%)0.3%
Missing27
Missing (%)< 0.1%
Memory size14.5 MiB
Minimum2002-02-22 00:00:00
Maximum2054-10-29 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-26T17:25:19.602118image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:25:19.796988image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

AÑO_FVENCE
Real number (ℝ)

High correlation  Skewed 

Distinct55
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.1879
Minimum2002
Maximum4743
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 MiB
2025-02-26T17:25:19.993766image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum2002
5-th percentile2004
Q12011
median2016
Q32021
95-th percentile2026
Maximum4743
Range2741
Interquartile range (IQR)10

Descriptive statistics

Standard deviation11.099629
Coefficient of variation (CV)0.0055052552
Kurtosis32579.44
Mean2016.1879
Median Absolute Deviation (MAD)5
Skewness133.376
Sum3.8395355 × 109
Variance123.20176
MonotonicityNot monotonic
2025-02-26T17:25:20.195005image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2024 116596
 
6.1%
2016 111666
 
5.9%
2020 106864
 
5.6%
2018 105796
 
5.6%
2019 101529
 
5.3%
2017 100515
 
5.3%
2015 95020
 
5.0%
2011 94356
 
5.0%
2023 92184
 
4.8%
2012 91973
 
4.8%
Other values (45) 887855
46.6%
ValueCountFrequency (%)
2002 30608
 
1.6%
2003 37320
 
2.0%
2004 37720
 
2.0%
2005 39186
2.1%
2006 39002
2.0%
2007 42266
2.2%
2008 50854
2.7%
2009 73040
3.8%
2010 76204
4.0%
2011 94356
5.0%
ValueCountFrequency (%)
4743 17
 
< 0.1%
2999 2
 
< 0.1%
2282 3
 
< 0.1%
2278 4
 
< 0.1%
2277 1
 
< 0.1%
2054 185
 
< 0.1%
2053 2826
0.1%
2051 8
 
< 0.1%
2050 2782
0.1%
2047 6626
0.3%

MES_FVENCE
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7017739
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-02-26T17:25:20.371547image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4686622
Coefficient of variation (CV)0.51757374
Kurtosis-1.2026459
Mean6.7017739
Median Absolute Deviation (MAD)3
Skewness-0.069282275
Sum12762550
Variance12.031618
MonotonicityNot monotonic
2025-02-26T17:25:20.549253image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
11 184221
9.7%
12 177352
9.3%
7 168007
8.8%
6 164169
8.6%
5 162557
8.5%
1 159003
8.3%
10 158948
8.3%
8 154410
8.1%
3 153205
8.0%
9 152267
8.0%
Other values (2) 270215
14.2%
ValueCountFrequency (%)
1 159003
8.3%
2 130297
6.8%
3 153205
8.0%
4 139918
7.3%
5 162557
8.5%
6 164169
8.6%
7 168007
8.8%
8 154410
8.1%
9 152267
8.0%
10 158948
8.3%
ValueCountFrequency (%)
12 177352
9.3%
11 184221
9.7%
10 158948
8.3%
9 152267
8.0%
8 154410
8.1%
7 168007
8.8%
6 164169
8.6%
5 162557
8.5%
4 139918
7.3%
3 153205
8.0%

DIA_FVENCE
Real number (ℝ)

High correlation 

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.73511
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-02-26T17:25:20.717699image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8171836
Coefficient of variation (CV)0.56035093
Kurtosis-1.2087605
Mean15.73511
Median Absolute Deviation (MAD)8
Skewness0.040999184
Sum29965220
Variance77.742726
MonotonicityNot monotonic
2025-02-26T17:25:20.910241image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
5 83035
 
4.4%
22 70880
 
3.7%
9 69666
 
3.7%
7 68909
 
3.6%
3 68159
 
3.6%
13 66982
 
3.5%
16 65224
 
3.4%
17 63751
 
3.3%
14 63441
 
3.3%
23 63391
 
3.3%
Other values (21) 1220916
64.1%
ValueCountFrequency (%)
1 50260
2.6%
2 54847
2.9%
3 68159
3.6%
4 61839
3.2%
5 83035
4.4%
6 59392
3.1%
7 68909
3.6%
8 61042
3.2%
9 69666
3.7%
10 60704
3.2%
ValueCountFrequency (%)
31 42775
2.2%
30 60176
3.2%
29 62175
3.3%
28 59697
3.1%
27 62056
3.3%
26 62561
3.3%
25 54934
2.9%
24 58811
3.1%
23 63391
3.3%
22 70880
3.7%

IINSTR
Categorical

High cardinality  Imbalance 

Distinct132
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
1108611 
PAGNAFIN
377164 
BONOS
 
97511
CETESI
 
78124
CBIC
 
58118
Other values (127)
184826 

Length

Max length15
Median length1
Mean length3.4426031
Min length1

Characters and Unicode

Total characters6555935
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowDEPBANX2
2nd rowDEPBANX2
3rd rowDEPBANX2
4th rowDEPBANX2
5th rowDEPBANX2

Common Values

ValueCountFrequency (%)
1108611
58.2%
PAGNAFIN 377164
 
19.8%
BONOS 97511
 
5.1%
CETESI 78124
 
4.1%
CBIC 58118
 
3.1%
UDIBONO 41263
 
2.2%
PAGARE 25632
 
1.3%
BPA182 22649
 
1.2%
BONDESD 22555
 
1.2%
BONDESF 9015
 
0.5%
Other values (122) 63712
 
3.3%

Length

2025-02-26T17:25:21.104639image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pagnafin 377164
47.5%
bonos 97511
 
12.3%
cetesi 78124
 
9.8%
cbic 58118
 
7.3%
udibono 41263
 
5.2%
pagare 25632
 
3.2%
bpa182 22649
 
2.9%
bondesd 22555
 
2.8%
bondesf 9015
 
1.1%
cerburudi 7774
 
1.0%
Other values (122) 53723
 
6.8%

Most occurring characters

ValueCountFrequency (%)
1126619
17.2%
N 931943
14.2%
A 852545
13.0%
I 569698
8.7%
P 450965
6.9%
G 416907
 
6.4%
F 388503
 
5.9%
O 316622
 
4.8%
B 308759
 
4.7%
E 259713
 
4.0%
Other values (24) 933661
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6555935
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1126619
17.2%
N 931943
14.2%
A 852545
13.0%
I 569698
8.7%
P 450965
6.9%
G 416907
 
6.4%
F 388503
 
5.9%
O 316622
 
4.8%
B 308759
 
4.7%
E 259713
 
4.0%
Other values (24) 933661
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6555935
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1126619
17.2%
N 931943
14.2%
A 852545
13.0%
I 569698
8.7%
P 450965
6.9%
G 416907
 
6.4%
F 388503
 
5.9%
O 316622
 
4.8%
B 308759
 
4.7%
E 259713
 
4.0%
Other values (24) 933661
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6555935
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1126619
17.2%
N 931943
14.2%
A 852545
13.0%
I 569698
8.7%
P 450965
6.9%
G 416907
 
6.4%
F 388503
 
5.9%
O 316622
 
4.8%
B 308759
 
4.7%
E 259713
 
4.0%
Other values (24) 933661
14.2%

ITINSTR
Categorical

High correlation  Imbalance 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
GUBERR
1407349 
BANCAR
419905 
OTROSRET
 
61293
GUBER
 
14948
OTROS
 
782
Other values (8)
 
77

Length

Max length9
Median length6
Mean length6.0561718
Min length5

Characters and Unicode

Total characters11533095
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowOTROS
2nd rowOTROS
3rd rowOTROS
4th rowOTROS
5th rowOTROS

Common Values

ValueCountFrequency (%)
GUBERR 1407349
73.9%
BANCAR 419905
 
22.0%
OTROSRET 61293
 
3.2%
GUBER 14948
 
0.8%
OTROS 782
 
< 0.1%
BANCARUS 53
 
< 0.1%
ICUSA 6
 
< 0.1%
PRIVADO 6
 
< 0.1%
GAMEXB 5
 
< 0.1%
GUBERN 4
 
< 0.1%
Other values (3) 3
 
< 0.1%

Length

2025-02-26T17:25:21.462130image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
guberr 1407349
73.9%
bancar 419905
 
22.0%
otrosret 61293
 
3.2%
guber 14948
 
0.8%
otros 782
 
< 0.1%
bancarus 53
 
< 0.1%
icusa 6
 
< 0.1%
privado 6
 
< 0.1%
gamexb 5
 
< 0.1%
gubern 4
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
R 3372986
29.2%
B 1842266
16.0%
E 1483599
12.9%
U 1422360
12.3%
G 1422306
12.3%
A 839936
 
7.3%
N 419966
 
3.6%
C 419966
 
3.6%
O 124160
 
1.1%
T 123371
 
1.1%
Other values (8) 62179
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11533095
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 3372986
29.2%
B 1842266
16.0%
E 1483599
12.9%
U 1422360
12.3%
G 1422306
12.3%
A 839936
 
7.3%
N 419966
 
3.6%
C 419966
 
3.6%
O 124160
 
1.1%
T 123371
 
1.1%
Other values (8) 62179
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11533095
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 3372986
29.2%
B 1842266
16.0%
E 1483599
12.9%
U 1422360
12.3%
G 1422306
12.3%
A 839936
 
7.3%
N 419966
 
3.6%
C 419966
 
3.6%
O 124160
 
1.1%
T 123371
 
1.1%
Other values (8) 62179
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11533095
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 3372986
29.2%
B 1842266
16.0%
E 1483599
12.9%
U 1422360
12.3%
G 1422306
12.3%
A 839936
 
7.3%
N 419966
 
3.6%
C 419966
 
3.6%
O 124160
 
1.1%
T 123371
 
1.1%
Other values (8) 62179
 
0.5%

NTINSTR
Categorical

High correlation  Imbalance 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
PAPEL GUBERNAMENTAL
1422301 
PAPELES BANCARIOS EN GRAL
419906 
Instrumentos con retenci�
 
61293
Otros Instrumentos
 
782
PAPEL BANCARIO USD
 
53
Other values (5)
 
19

Length

Max length25
Median length19
Mean length20.515649
Min length7

Characters and Unicode

Total characters39069059
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowOtros Instrumentos
2nd rowOtros Instrumentos
3rd rowOtros Instrumentos
4th rowOtros Instrumentos
5th rowOtros Instrumentos

Common Values

ValueCountFrequency (%)
PAPEL GUBERNAMENTAL 1422301
74.7%
PAPELES BANCARIOS EN GRAL 419906
 
22.0%
Instrumentos con retenci� 61293
 
3.2%
Otros Instrumentos 782
 
< 0.1%
PAPEL BANCARIO USD 53
 
< 0.1%
PAPEL COMERCIAL ICUSA 6
 
< 0.1%
PRIVADO 6
 
< 0.1%
ABS AMERICAN EXPRESS BANK 5
 
< 0.1%
Instrumentos con man fisc 1
 
< 0.1%
Otros Insrumentos 1
 
< 0.1%

Length

2025-02-26T17:25:21.650881image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-26T17:25:21.832346image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
papel 1422360
30.2%
gubernamental 1422301
30.2%
papeles 419906
 
8.9%
bancarios 419906
 
8.9%
en 419906
 
8.9%
gral 419906
 
8.9%
instrumentos 62076
 
1.3%
con 61294
 
1.3%
retenci� 61293
 
1.3%
otros 783
 
< 0.1%
Other values (12) 147
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 5946730
15.2%
E 5526701
14.1%
P 3684543
9.4%
L 3684479
9.4%
N 3684477
9.4%
2805524
7.2%
R 2262188
 
5.8%
B 1842270
 
4.7%
G 1842207
 
4.7%
U 1422360
 
3.6%
Other values (23) 6367580
16.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39069059
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 5946730
15.2%
E 5526701
14.1%
P 3684543
9.4%
L 3684479
9.4%
N 3684477
9.4%
2805524
7.2%
R 2262188
 
5.8%
B 1842270
 
4.7%
G 1842207
 
4.7%
U 1422360
 
3.6%
Other values (23) 6367580
16.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39069059
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 5946730
15.2%
E 5526701
14.1%
P 3684543
9.4%
L 3684479
9.4%
N 3684477
9.4%
2805524
7.2%
R 2262188
 
5.8%
B 1842270
 
4.7%
G 1842207
 
4.7%
U 1422360
 
3.6%
Other values (23) 6367580
16.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39069059
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 5946730
15.2%
E 5526701
14.1%
P 3684543
9.4%
L 3684479
9.4%
N 3684477
9.4%
2805524
7.2%
R 2262188
 
5.8%
B 1842270
 
4.7%
G 1842207
 
4.7%
U 1422360
 
3.6%
Other values (23) 6367580
16.3%

TASA
Real number (ℝ)

Skewed  Zeros 

Distinct51965
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1421575
Minimum-99.251541
Maximum999
Zeros55531
Zeros (%)2.9%
Negative203
Negative (%)< 0.1%
Memory size7.3 MiB
2025-02-26T17:25:22.062811image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-99.251541
5-th percentile2.98
Q14.2600002
median5.75
Q37.7800002
95-th percentile11.157175
Maximum999
Range1098.2516
Interquartile range (IQR)3.52

Descriptive statistics

Standard deviation4.7912083
Coefficient of variation (CV)0.78005298
Kurtosis18134.322
Mean6.1421575
Median Absolute Deviation (MAD)1.75
Skewness104.61774
Sum11696842
Variance22.955675
MonotonicityNot monotonic
2025-02-26T17:25:22.261495image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 55531
 
2.9%
4.5 32327
 
1.7%
4.480000019 26860
 
1.4%
4.489999771 25477
 
1.3%
7 18947
 
1.0%
4.550000191 18472
 
1.0%
4.46999979 18187
 
1.0%
4 16143
 
0.8%
4.519999981 15241
 
0.8%
7.5 14110
 
0.7%
Other values (51955) 1663059
87.3%
ValueCountFrequency (%)
-99.25154114 4
< 0.1%
-9.854305267 1
 
< 0.1%
-9.722984314 2
< 0.1%
-9.660676003 1
 
< 0.1%
-9.607212067 1
 
< 0.1%
-5.959506989 1
 
< 0.1%
-4.300000191 1
 
< 0.1%
-3.5 1
 
< 0.1%
-3.349999905 1
 
< 0.1%
-3.339999914 2
< 0.1%
ValueCountFrequency (%)
999 2
< 0.1%
935 2
< 0.1%
931 2
< 0.1%
927 2
< 0.1%
926 2
< 0.1%
923 2
< 0.1%
905 2
< 0.1%
893 2
< 0.1%
875 2
< 0.1%
860 2
< 0.1%

MONEDA
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
MXP
1904301 
USD
 
53

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5713062
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMXP
2nd rowMXP
3rd rowMXP
4th rowMXP
5th rowMXP

Common Values

ValueCountFrequency (%)
MXP 1904301
> 99.9%
USD 53
 
< 0.1%

Length

2025-02-26T17:25:22.471738image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-26T17:25:22.628188image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
mxp 1904301
> 99.9%
usd 53
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
M 1904301
33.3%
X 1904301
33.3%
P 1904301
33.3%
U 53
 
< 0.1%
S 53
 
< 0.1%
D 53
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5713062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 1904301
33.3%
X 1904301
33.3%
P 1904301
33.3%
U 53
 
< 0.1%
S 53
 
< 0.1%
D 53
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5713062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 1904301
33.3%
X 1904301
33.3%
P 1904301
33.3%
U 53
 
< 0.1%
S 53
 
< 0.1%
D 53
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5713062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 1904301
33.3%
X 1904301
33.3%
P 1904301
33.3%
U 53
 
< 0.1%
S 53
 
< 0.1%
D 53
 
< 0.1%

MONTO
Real number (ℝ)

High correlation  Skewed 

Distinct1185442
Distinct (%)62.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0257022 × 108
Minimum0
Maximum1.5641229 × 1012
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.3 MiB
2025-02-26T17:25:22.778661image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile69230.9
Q18348277.5
median95858232
Q37 × 108
95-th percentile5 × 109
Maximum1.5641229 × 1012
Range1.5641229 × 1012
Interquartile range (IQR)6.9165172 × 108

Descriptive statistics

Standard deviation3.2359094 × 109
Coefficient of variation (CV)3.5852162
Kurtosisinf
Mean9.0257022 × 108
Median Absolute Deviation (MAD)95333456
Skewness133.80548
Sum1.7188132 × 1015
Variance1.047111 × 1019
MonotonicityNot monotonic
2025-02-26T17:25:22.995538image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000000000 40313
 
2.1%
2000000000 27701
 
1.5%
1000000000 25666
 
1.3%
999999936 22950
 
1.2%
3000000000 18497
 
1.0%
500000000 14904
 
0.8%
1999999872 11370
 
0.6%
2500000000 10936
 
0.6%
1500000000 10276
 
0.5%
4000000000 10272
 
0.5%
Other values (1185432) 1711469
89.9%
ValueCountFrequency (%)
0 3
< 0.1%
0.009999999776 3
< 0.1%
0.01999999955 6
< 0.1%
0.1000000015 1
 
< 0.1%
0.5699999928 1
 
< 0.1%
0.7699999809 1
 
< 0.1%
0.8643058538 1
 
< 0.1%
0.8650169969 2
 
< 0.1%
0.8652539849 2
 
< 0.1%
0.865490973 2
 
< 0.1%
ValueCountFrequency (%)
1.564122939 × 10121
 
< 0.1%
9.999999959 × 10113
< 0.1%
6.500000072 × 10112
< 0.1%
5.999999713 × 10111
 
< 0.1%
4.99999998 × 10111
 
< 0.1%
4.365649838 × 10112
< 0.1%
4.337648599 × 10111
 
< 0.1%
4.013876838 × 10112
< 0.1%
4.000000573 × 10112
< 0.1%
3.231570985 × 10111
 
< 0.1%

MONTO_ASIGNADO
Real number (ℝ)

High correlation  Zeros 

Distinct1188270
Distinct (%)62.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7885802 × 108
Minimum-13579079
Maximum1.4667758 × 1011
Zeros28357
Zeros (%)1.5%
Negative2
Negative (%)< 0.1%
Memory size7.3 MiB
2025-02-26T17:25:23.203635image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-13579079
5-th percentile17244.902
Q17041167.4
median89004636
Q36.7887302 × 108
95-th percentile5 × 109
Maximum1.4667758 × 1011
Range1.4669116 × 1011
Interquartile range (IQR)6.7183186 × 108

Descriptive statistics

Standard deviation2.4079949 × 109
Coefficient of variation (CV)2.7399134
Kurtosisinf
Mean8.7885802 × 108
Median Absolute Deviation (MAD)88721304
Skewness11.049052
Sum1.6736568 × 1015
Variance5.7984395 × 1018
MonotonicityNot monotonic
2025-02-26T17:25:23.402941image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000000000 39507
 
2.1%
0 28357
 
1.5%
2000000000 26842
 
1.4%
1000000000 24188
 
1.3%
999999936 23038
 
1.2%
3000000000 18055
 
0.9%
500000000 13363
 
0.7%
1999999872 11420
 
0.6%
2500000000 10640
 
0.6%
4000000000 10047
 
0.5%
Other values (1188260) 1698897
89.2%
ValueCountFrequency (%)
-13579079 1
 
< 0.1%
-400016.4688 1
 
< 0.1%
0 28357
1.5%
0.7384160161 1
 
< 0.1%
0.7411739826 1
 
< 0.1%
0.7855070233 1
 
< 0.1%
0.8035119772 1
 
< 0.1%
0.8052319884 1
 
< 0.1%
0.8069210052 1
 
< 0.1%
0.8101329803 1
 
< 0.1%
ValueCountFrequency (%)
1.466775798 × 10111
< 0.1%
1.453221478 × 10111
< 0.1%
1.392402596 × 10111
< 0.1%
1.333044593 × 10111
< 0.1%
1.32204503 × 10111
< 0.1%
1.320375173 × 10111
< 0.1%
1.301427323 × 10111
< 0.1%
1.301411103 × 10111
< 0.1%
1.293893304 × 10111
< 0.1%
1.274216448 × 10111
< 0.1%

MONTO_REAL
Real number (ℝ)

High correlation  Skewed 

Distinct1186581
Distinct (%)62.3%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean9.0237322 × 108
Minimum0
Maximum1.0031718 × 1012
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.3 MiB
2025-02-26T17:25:23.637721image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile69254.684
Q18360782.5
median95924536
Q37 × 108
95-th percentile5 × 109
Maximum1.0031718 × 1012
Range1.0031718 × 1012
Interquartile range (IQR)6.9163922 × 108

Descriptive statistics

Standard deviation3.0857713 × 109
Coefficient of variation (CV)3.4196175
Kurtosisinf
Mean9.0237322 × 108
Median Absolute Deviation (MAD)95399480
Skewness94.710037
Sum1.7184353 × 1015
Variance9.521984 × 1018
MonotonicityNot monotonic
2025-02-26T17:25:23.840831image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000000000 40294
 
2.1%
2000000000 27682
 
1.5%
1000000000 25608
 
1.3%
999999936 22950
 
1.2%
3000000000 18485
 
1.0%
500000000 14762
 
0.8%
1999999872 11371
 
0.6%
2500000000 10933
 
0.6%
4000000000 10268
 
0.5%
1500000000 10256
 
0.5%
Other values (1186571) 1711742
89.9%
ValueCountFrequency (%)
0 4
< 0.1%
0.009999999776 3
< 0.1%
0.01999999955 6
< 0.1%
0.1000000015 1
 
< 0.1%
0.5699999928 1
 
< 0.1%
0.6271209717 1
 
< 0.1%
0.7699999809 1
 
< 0.1%
0.8643058538 1
 
< 0.1%
0.8650169969 2
 
< 0.1%
0.8652539849 2
 
< 0.1%
ValueCountFrequency (%)
1.003171807 × 10121
< 0.1%
9.999999959 × 10112
< 0.1%
7.804609495 × 10111
< 0.1%
6.500000072 × 10112
< 0.1%
5.999999713 × 10111
< 0.1%
4.99999998 × 10111
< 0.1%
4.365649838 × 10112
< 0.1%
4.337275372 × 10111
< 0.1%
4.013876838 × 10112
< 0.1%
4.000000573 × 10112
< 0.1%
Distinct5742
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size14.5 MiB
Minimum2002-01-21 00:00:00
Maximum2024-11-29 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-26T17:25:24.026151image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:25:24.224559image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2025-02-26T17:24:54.520674image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:25.771152image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:30.621812image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:35.375925image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:40.225072image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:45.244298image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:49.953731image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:54.768849image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:59.698213image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:04.471606image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:09.401751image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:14.352429image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:19.319439image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:24.601088image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:29.710433image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:34.853769image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:39.780084image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:44.272244image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:48.931812image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:54.760905image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:26.018767image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:30.880285image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:35.641525image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:40.526905image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:45.496304image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:50.223184image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:55.174659image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:59.928436image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:04.719512image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:09.646092image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:14.624205image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:19.565680image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:24.848525image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:29.984597image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:35.108037image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:40.188759image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:44.511815image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:49.190189image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:55.041126image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:26.271304image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:31.103949image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:35.885058image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:40.917979image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:45.716275image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:50.447652image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:55.434723image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:00.161887image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:04.985211image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:09.912535image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:14.902858image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:19.796076image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:25.100255image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:30.248841image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:35.361241image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:40.443604image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:44.764648image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:49.462454image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2025-02-26T17:24:17.702272image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:22.799298image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:27.975169image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:33.228572image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:38.319388image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:42.898932image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:47.395365image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:52.596112image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:58.620547image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:29.317410image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:34.045246image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:38.922746image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:43.926151image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:48.702411image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:53.483531image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:58.425881image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:03.192857image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:07.949681image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:12.945272image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:17.939549image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:23.046298image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:28.262980image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:33.521236image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:38.558743image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:43.121838image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:47.615462image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:52.923385image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:58.915627image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:29.553308image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:34.290287image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:39.160400image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:44.171580image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:48.939664image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:53.728583image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:58.658876image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:03.459316image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:08.220849image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:13.208799image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:18.216974image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:23.337709image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:28.544744image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:33.793776image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:38.770566image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:43.340595image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:47.841297image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:53.231004image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:59.221028image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:29.814014image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:34.541046image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:39.418944image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:44.426323image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:49.189634image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:53.981390image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:58.921715image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:03.702573image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:08.456876image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:13.471801image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:18.480035image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:23.614830image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:28.804970image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:34.046388image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:39.014128image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:43.568648image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:48.090061image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:53.519691image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:59.528763image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:30.111733image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:34.828613image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:39.675578image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:44.677059image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:49.457173image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:54.238076image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:59.165334image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:03.941424image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:08.713260image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:13.757987image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:18.769478image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:23.892270image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:29.102889image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:34.304831image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:39.284657image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:43.811351image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:48.339848image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:53.786229image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:59.886048image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:30.380850image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:35.115509image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:39.952335image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:44.979091image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:49.716274image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:54.510048image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:23:59.439126image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:04.217982image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:09.164301image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:14.082201image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:19.062697image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:24.170812image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:29.427362image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:34.588579image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:39.563195image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:44.058747image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:48.611927image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-26T17:24:54.238140image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Correlations

2025-02-26T17:25:24.415634image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
AÑO_FLIQAÑO_FVENCEAÑO_OPECPZO_ACPZO_DEDIA_FLIQDIA_FVENCEDIA_OPEESPROVEEDICONTRATOIORDENITINSTRMES_FLIQMES_FVENCEMES_OPEMONEDAMONTOMONTO_ASIGNADOMONTO_REALNTINSTRNUMERO_CLIENTEPLAZOREFTASATIPO_PERSONA
AÑO_FLIQ1.0000.9301.0000.0370.0370.002-0.004-0.0020.1160.3500.0340.222-0.027-0.017-0.0270.010-0.102-0.101-0.1020.0860.3500.0560.2900.097
AÑO_FVENCE0.9301.0000.9300.2240.2240.003-0.0280.0000.0030.2970.0190.042-0.0210.031-0.0220.000-0.141-0.138-0.1400.0420.2970.0980.2550.000
AÑO_OPE1.0000.9301.0000.0370.0370.002-0.004-0.0020.1160.3500.0340.222-0.027-0.017-0.0270.010-0.102-0.101-0.1020.0860.3500.0560.2900.097
CPZO_A0.0370.2240.0371.0001.0000.006-0.0310.0030.002-0.0680.0320.0370.0020.089-0.0000.000-0.219-0.215-0.2180.038-0.0680.2960.0480.000
CPZO_DE0.0370.2240.0371.0001.0000.006-0.0310.0030.002-0.0680.0320.0370.0020.089-0.0000.000-0.219-0.215-0.2180.038-0.0680.2960.0480.000
DIA_FLIQ0.0020.0030.0020.0060.0061.0000.5670.9680.0040.0000.0060.005-0.005-0.002-0.0030.004-0.005-0.006-0.0050.0050.0000.0030.0020.004
DIA_FVENCE-0.004-0.028-0.004-0.031-0.0310.5671.0000.5670.039-0.0090.0070.0250.006-0.0260.0060.0030.0180.0170.0180.025-0.0090.009-0.0140.029
DIA_OPE-0.0020.000-0.0020.0030.0030.9680.5671.0000.005-0.0000.0070.005-0.002-0.002-0.0040.003-0.005-0.006-0.0050.005-0.0000.0010.0010.005
ESPROVEED0.1160.0030.1160.0020.0020.0040.0390.0051.0000.1240.1690.0630.0090.0260.0090.0000.0000.0350.0050.0610.1240.0010.0670.500
ICONTRATO0.3500.2970.350-0.068-0.0680.000-0.009-0.0000.1241.0000.0990.3370.0050.0010.0050.456-0.294-0.278-0.2940.3361.0000.0080.1400.445
IORDEN0.0340.0190.0340.0320.0320.0060.0070.0070.1690.0991.0000.080-0.001-0.006-0.0010.008-0.118-0.110-0.1180.0330.0990.0430.1520.081
ITINSTR0.2220.0420.2220.0370.0370.0050.0250.0050.0630.3370.0801.0000.0130.0330.0131.0000.0000.0100.0001.0000.3370.0440.0280.304
MES_FLIQ-0.027-0.021-0.0270.0020.002-0.0050.006-0.0020.0090.005-0.0010.0131.0000.7820.9990.003-0.003-0.004-0.0030.0050.005-0.004-0.0050.009
MES_FVENCE-0.0170.031-0.0170.0890.089-0.002-0.026-0.0020.0260.001-0.0060.0330.7821.0000.7820.004-0.045-0.044-0.0450.0300.001-0.012-0.0070.030
MES_OPE-0.027-0.022-0.027-0.000-0.000-0.0030.006-0.0040.0090.005-0.0010.0130.9990.7821.0000.004-0.003-0.004-0.0030.0060.005-0.004-0.0050.009
MONEDA0.0100.0000.0100.0000.0000.0040.0030.0030.0000.4560.0081.0000.0030.0040.0041.0000.0000.0000.0001.0000.4560.0000.0000.006
MONTO-0.102-0.141-0.102-0.219-0.219-0.0050.018-0.0050.000-0.294-0.1180.000-0.003-0.045-0.0030.0001.0000.9651.0000.000-0.294-0.078-0.0620.001
MONTO_ASIGNADO-0.101-0.138-0.101-0.215-0.215-0.0060.017-0.0060.035-0.278-0.1100.010-0.004-0.044-0.0040.0000.9651.0000.9650.010-0.278-0.079-0.0680.046
MONTO_REAL-0.102-0.140-0.102-0.218-0.218-0.0050.018-0.0050.005-0.294-0.1180.000-0.003-0.045-0.0030.0001.0000.9651.0000.000-0.294-0.078-0.0620.004
NTINSTR0.0860.0420.0860.0380.0380.0050.0250.0050.0610.3360.0331.0000.0050.0300.0061.0000.0000.0100.0001.0000.3360.0440.0260.304
NUMERO_CLIENTE0.3500.2970.350-0.068-0.0680.000-0.009-0.0000.1241.0000.0990.3370.0050.0010.0050.456-0.294-0.278-0.2940.3361.0000.0080.1400.445
PLAZOREF0.0560.0980.0560.2960.2960.0030.0090.0010.0010.0080.0430.044-0.004-0.012-0.0040.000-0.078-0.079-0.0780.0440.0081.0000.1070.000
TASA0.2900.2550.2900.0480.0480.002-0.0140.0010.0670.1400.1520.028-0.005-0.007-0.0050.000-0.062-0.068-0.0620.0260.1400.1071.0000.043
TIPO_PERSONA0.0970.0000.0970.0000.0000.0040.0290.0050.5000.4450.0810.3040.0090.0300.0090.0060.0010.0460.0040.3040.4450.0000.0431.000

Missing values

2025-02-26T17:25:00.178288image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-26T17:25:04.980662image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-26T17:25:11.485842image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

NUMERO_CLIENTEESPROVEEDTIPO_PERSONANOMBRENOMLARGORFCNLINEAICONTRATOIORDENFOPERAÑO_OPEMES_OPEDIA_OPEFLIQAÑO_FLIQMES_FLIQDIA_FLIQCPZO_DECPZO_APLAZOREFFVENCEAÑO_FVENCEMES_FVENCEDIA_FVENCEIINSTRITINSTRNTINSTRTASAMONEDAMONTOMONTO_ASIGNADOMONTO_REALFECHA_REGISTRO
01040153TrueMoral Nacional no GravableBANXICOBANCO DE MEXICOBNM840515VB20104015310401534302152020-11-12202011122020-11-12 00:00:00202011124942494212034-05-252034525DEPBANX2OTROSOtros Instrumentos4.25MXP3.302570e+093.302570e+093.302570e+092020-11-12
151000FalseCuentas PropiasGARANTIAS CREDITOGARANTIAS CREDITONFI3406305T000051000510004302142020-11-12202011122020-11-12 00:00:00202011124942494212034-05-252034525DEPBANX2OTROSOtros Instrumentos4.25MXP3.297121e+093.297121e+093.297121e+092020-11-12
21040153TrueMoral Nacional no GravableBANXICOBANCO DE MEXICOBNM840515VB20104015310401534301002020-11-12202011122020-11-12 00:00:00202011124942494212034-05-252034525DEPBANX2OTROSOtros Instrumentos4.25MXP3.302570e+090.000000e+003.308033e+092020-11-12
351000FalseCuentas PropiasGARANTIAS CREDITOGARANTIAS CREDITONFI3406305T000051000510004300632020-11-12202011122020-11-12 00:00:00202011124942494212034-05-252034525DEPBANX2OTROSOtros Instrumentos4.25MXP3.302570e+090.000000e+003.308033e+092020-11-12
41040153TrueMoral Nacional no GravableBANXICOBANCO DE MEXICOBNM840515VB20104015310401533290692020-04-012020412020-04-01 00:00:002020415167516712034-05-252034525DEPBANX2OTROSOtros Instrumentos6.50MXP5.217592e+095.217592e+095.217592e+092020-04-01
551000FalseCuentas PropiasGARANTIAS CREDITOGARANTIAS CREDITONFI3406305T000051000510003290452020-04-012020412020-04-01 00:00:002020415167516712034-05-252034525DEPBANX2OTROSOtros Instrumentos6.50MXP5.205281e+095.205281e+095.205281e+092020-04-01
651000FalseCuentas PropiasGARANTIAS CREDITOGARANTIAS CREDITONFI3406305T000051000510001363132014-11-0620141162014-11-06 00:00:0020141167140714012034-05-252034525DEPBANX2OTROSOtros Instrumentos3.00MXP3.408039e+083.408039e+083.408039e+082014-11-06
71040153TrueMoral Nacional no GravableBANXICOBANCO DE MEXICOBNM840515VB20104015310401531363082014-11-0620141162014-11-06 00:00:0020141167140714012034-05-252034525DEPBANX2OTROSOtros Instrumentos3.00MXP3.408039e+083.408039e+083.408039e+082014-11-06
851000FalseCuentas PropiasGARANTIAS CREDITOGARANTIAS CREDITONFI3406305T000051000510001213652014-10-0920141092014-10-09 00:00:0020141097168716812034-05-252034525DEPBANX2OTROSOtros Instrumentos3.00MXP3.408038e+083.408038e+083.408038e+082014-10-09
91040153TrueMoral Nacional no GravableBANXICOBANCO DE MEXICOBNM840515VB20104015310401531213302014-10-0920141092014-10-09 00:00:0020141097168716812034-05-252034525DEPBANX2OTROSOtros Instrumentos3.00MXP3.408038e+083.408038e+083.408038e+082014-10-09
NUMERO_CLIENTEESPROVEEDTIPO_PERSONANOMBRENOMLARGORFCNLINEAICONTRATOIORDENFOPERAÑO_OPEMES_OPEDIA_OPEFLIQAÑO_FLIQMES_FLIQDIA_FLIQCPZO_DECPZO_APLAZOREFFVENCEAÑO_FVENCEMES_FVENCEDIA_FVENCEIINSTRITINSTRNTINSTRTASAMONEDAMONTOMONTO_ASIGNADOMONTO_REALFECHA_REGISTRO
19043441064769TrueMoral Nacional no GravableCJF FISO 80692NAFIN FIDEICOMISO PENSIONES COMPLEMENTARIAS DE MAGISTRADOS Y JUECES JUBCJF950204TL0106476910647691525632024-11-26202411262024-11-27 00:00:0020241127363612025-01-02202512BONDESFGUBERRPAPEL GUBERNAMENTAL10.372MXP1.005706e+081.005706e+081.005706e+082024-11-26
19043451064769TrueMoral Nacional no GravableCJF FISO 80692NAFIN FIDEICOMISO PENSIONES COMPLEMENTARIAS DE MAGISTRADOS Y JUECES JUBCJF950204TL0106476910647691525502024-11-26202411262024-11-27 00:00:0020241127929212025-02-272025227BONDESFGUBERRPAPEL GUBERNAMENTAL10.375MXP1.005588e+081.005588e+081.005588e+082024-11-26
19043461064811TrueMoral Nacional no GravableTEP FISO 80694NAFIN, FIDEICOMISO DE ADMON Y FUENTE DE PAGO 80694, FISO APOYOS MEDICOSTEP961122B8A106481110648111525482024-11-26202411262024-11-27 00:00:0020241127363612025-01-02202512BONDESFGUBERRPAPEL GUBERNAMENTAL10.372MXP9.051352e+059.051352e+059.051352e+052024-11-26
19043471064806TrueMoral Nacional no GravableCJF FISO 80694NAFIN FIDEICOMISO APOYOS MED. COMPLEMENTARIOS Y APOYO ECONOMICO EXT PJFCJF950204TL0106480610648061525462024-11-26202411262024-11-27 00:00:0020241127363612025-01-02202512BONDESFGUBERRPAPEL GUBERNAMENTAL10.372MXP1.609129e+061.609129e+061.609129e+062024-11-26
19043481064796TrueMoral Nacional no GravableCJF FISO 80693NAFIN, FISO MANTENIMIENTO DE CASA HABITACION DE MAGISTRADOS Y JUECECJF950204TL0106479610647961525432024-11-26202411262024-11-27 00:00:0020241127363612025-01-02202512BONDESFGUBERRPAPEL GUBERNAMENTAL10.372MXP2.514264e+062.514264e+062.514264e+062024-11-26
19043491053131TrueMoral Nacional no GravableFEFAFONDO ESPECIAL PARA EL FINANCIAMIENTO AGROPECUARIO (FEFA)FEF650826K410105313110531312981612006-06-092006692006-06-09 00:00:002006697702006-06-162006616OTROSBANCInstrumentos con man fisc7.030MXP1.230000e+090.000000e+001.230000e+092006-06-09
19043501062813TrueMoral Nacional no GravableBLK / BLKLIQPBLK FONDEO GUBERNAMENTALES MEXICO EXENTOS, S.A. DE C.V., F.I.I.D.HLP010918LTA0106281310628131762852019-05-2420195242019-05-24 00:00:0020195243302019-05-272019527GUBERNPAPEL GUBERNAMENTAL7.360MXP6.529709e+080.000000e+006.529709e+082019-05-24
19043511064295TrueMoral Nacional no GravablePRINCIPAL PENSIONES CAPITAPRINCIPAL PENSIONES S.A. DE C.V. CAPITAL TPPE990528GD0106429510642951751082019-05-2320195232019-05-23 00:00:0020195234402019-05-272019527GUBERNPAPEL GUBERNAMENTAL8.260MXP5.520562e+040.000000e+005.520562e+042019-05-23
19043521063782TrueMoral Nacional no GravableAZT8589SIEFORE AZTECA BASICA 85-89 S.A. DE C.V.SAB080110UG5106378210637828947752018-07-052018752018-07-05 00:00:002018751102018-07-06201876GUBERNPAPEL GUBERNAMENTAL7.840MXP2.522300e+080.000000e+002.522300e+082018-07-05
19043531063238TrueMoral Nacional no GravableBLIQUIDBURSALIQUIDO, S.A. DE C.V. SOC DE INV EN INST DE DEUDABUR9001257R20106323810632383099062011-01-2120111212011-01-21 00:00:0020111213302011-01-242011124GUBERNPAPEL GUBERNAMENTAL4.550MXP7.212859e+050.000000e+007.212859e+052011-01-21